1. Technical Field
The present disclosure relates to registration validation and, more specifically, to methods and systems for validating non-rigid pulmonary image registration.
2. Discussion of Related Art
Registration is the process of identifying correspondence between multiple images. Within the field of medical imaging, registration of images may be used to combine image data across various imaging modalities, to compare an earlier-acquired image with a later-acquired image to determine change, or to compensate for patient motion over a long acquisition period or within a time series of medical images.
In motion compensation, registration may be used to associate anatomical structure from image frame to image frame, even as the relative size, shape and location of various anatomical elements changes as a result of such factors as respiration and cardiac cycle.
Where acquisition of a medical image requires several seconds or even several minutes to complete, motion compensation may be used to acquire the image even as cardiac motion continues. Motion compensation may also provide desirable results even in the absence of the subject holding breath.
Where multiple images are acquired as part of a time series, motion compensation may be used to provide a stable frame of reference so that various functional and/or pathological analyses may be more easily performed. For example, blood flow through pulmonary arteries may be more easily located and analyzed when the images over time are corrected for the respiratory motion.
There are multiple techniques available for performing image registration. Some techniques may provide better registration results than other techniques in various circumstances. As suboptimal registration may lead to poor results, it is often desirable to validate a registration to determine if the registration was well performed or if one registration algorithm is superior to the other in terms of different criteria.
However, it may be difficult to validate registration when the ground truth of how the anatomical structure shown within one image actually corresponds to the anatomical structure found within another image is not known. While in some instances it may be possible for an expert to manually provide this ground truth so that the registration may be more accurately validated, in certain situations, for example, the clinical setting and operation complexity, this option might not be practicable. Accordingly, methods for validating registration in the absence of knowledge of ground truth have been devised.
Consistency registration error (CRE) is one approach for validating registration in the absence of ground truth. This approach operates under the assumption that if a forward registration is determined (in which a first image is registered to a second image) and then a backward registration is determined (in which the second image is back-registered to the first image using the same registration technique), a successful registration technique for that particular age set should be able to move a given pixel from the domain of the first image to the domain of the second image using the forward registration and then move the pixel back to the domain of the first image using the backward registration such that the original location of the given pixel is not different from the new location of the given pixel after the forward and backward registrations are applied in sequence. In practice, CRE is computed as the distance between the original position of a point and the transformed position of the point after forward and backward registration. The smaller the difference the more consistent the registration is.